The content of this article comes from the blog of Amazon CTO Dr. Werner, the original link:
https://www.allthingsdistributed.com/2023/04/an-introduction-to-generative-ai-with-swami-sivasubramanian.html
In the past few months, generative AI and related underlying technologies have attracted a lot of attention. It has seeped into the collective consciousness of many, and it is discussed by everyone from board meetings to parent-teacher conferences. Lots of consumers are using it, and lots of businesses are trying to figure out how to take advantage of its potential. But it didn't just appear out of nowhere -- research on machine learning goes back decades. In fact, machine learning has long been our housekeeping skill at Amazon . It's used to personalize Amazon's retail site, control robots in our fulfillment centers, and power Alexa's intent recognition and speech synthesis. Machine learning is embedded in Amazon's DNA.
What we have achieved now is inseparable from some of our key technological advances. The first is cloud technology. This is the cornerstone that provides the massive amounts of computation and data required for deep learning. Then there are neural networks that can understand and learn patterns. It opens the door to complex algorithms, such as those used in image recognition. And finally Transformers. Different from RNN with linear sequence structure, Transformer can process multiple sequences in parallel, which greatly speeds up training, and can be used to create larger and more accurate models, so as to understand human knowledge, and can write poetry or even Such things as debugging code.
Recently, I had an in-depth conversation with my old friend Swami Sivasubramanian , who is in charge of database, data analysis and machine learning services of Amazon cloud technology. He was instrumental in the initial build of Dynamo and later in the worldwide adoption of NoSQL technology via Amazon DynamoDB. During our conversation, I learned about the vast promise of generative AI, how Amazon is making large language and base models easier to use, and how custom chips can help reduce costs, speed up training, and improve energy efficiency.
We're still in the early days, but as Swami said, large languages and underlying models will be a core part of every application in the next few years. I'm looking forward to seeing how build developers use this technology to innovate and solve hard problems.
I still remember that on the first day of Swami's employment 17 years ago, I gave him two simple tasks: help build a database that meets Amazon's scale and needs; and re-examine the company's data strategy. He said our first meeting was ambitious. But he did my job brilliantly.
If you want to read more about what the Swami team has achieved, you can read more here:
https://aws.amazon.com/blogs/machine-learning/announcing-new-tools-for-building-with-generative-ai-on-aws/
Now, as usual, start building!
I heard, click the 4 buttons below
You will not encounter bugs!
